Modern security and surveillance systems have come to rely very heavily on the use of video surveillance cameras for the monitoring of remote locations, entry/exit points of buildings or other restricted areas, and high-value assets, etc. The majority of surveillance video cameras that are in use today are analog. Analog video surveillance systems run coaxial cable from closed circuit television (CCTV) cameras to centrally located videotape recorders or hard drives. Increasingly, the resultant video footage is compressed on a digital video recorder (DVR) to save storage space. The use of digital video systems (DVS) is also increasing; in DVS, the analog video is digitized, compressed and packetized in IP, and then streamed to a server.
More recently, IP-networked digital video systems have been implemented. In this type of system the surveillance video is encoded directly on a digital camera, in H.264 or another suitable standard for video compression, and is sent over Ethernet at a bit rate. This transition from analog to digital video is bringing about long-awaited benefits to security and surveillance systems, largely because digital compression allows more video data to be transmitted and stored. Of course, a predictable result of capturing larger amounts of video data is that more personnel are required to review the video that is provided from the video surveillance cameras. Advantageously, storing the video can reduce the amount of video data that is to be reviewed, since the motion vectors and detectors that are used in compression can be used to eliminate those frames with no significant activity. However, since motion vectors and detectors offer no information as to what is occurring, someone still must physically screen the captured video to determine suspicious activity.
The market is currently seeing a migration toward IP-based hardware edge devices with built-in video analytics, such as IP cameras and encoders. Video analytics electronically recognizes the significant features within a series of frames and allows the system to issue alerts or take other actions when specific types of events occur, thereby speeding real-time security response, etc. Automatically searching the captured video for specific content also relieves personnel from tedious hours of reviewing the video, and decreases the number of personnel that is required to screen the video. Furthermore, when ‘smart’ cameras and encoders process images at the edge, they record or transmit only important events, for example only when someone enters a predefined area that is under surveillance, such as a perimeter along a fence. Accordingly, deploying an edge device is one method to reduce the strain on a network in terms of system requirements and bandwidth.
Unfortunately, deploying ‘smart’ cameras and encoders at the edge carries a significantly higher cost premium compared to deploying a similar number of basic digital or analog cameras. Furthermore, since the analytics within the cameras is designed into the cameras there is a tradeoff between flexibility and cost, with higher cost solutions providing more flexibility. In essence, to support changing functionality requires a new camera.
Greater flexibility and lower cost may also be achieved when video data is streamed locally to a centralized resource for video analytics processing. International patent publication number WO 2008/092255, which was published on 7 Aug. 2008, discloses a task-based video analytics processing approach in which video data is streamed from IP cameras or video recorders at the edge to shared co-located video analytics resources via a Local Area Network. In particular, a video analytics task manager routes video analytics tasks to a shared video analytics resource in response to a video analytics task request. The shared video analytics resource obtains video data to be analyzed in response to receipt of the video analytics task, and performs requested video analytics on the obtained video data. Since the video data is transmitted via a LAN, which is limited to a relatively small geographic area, it is a relatively simple matter to provide a network between the edge devices and the centralized processing facilities that has sufficient bandwidth to accommodate large amounts of video data. Unfortunately, such a system cannot be expanded easily to include very many additional edge devices since the processing capabilities of the system within the LAN are finite. Similarly, the ability to perform multiple video analytics functions in parallel is limited by the processing capabilities of the system. Simply adding more servers to process the video data from additional edge devices, or to process video data using a plurality of different video analytics engines, is very expensive in terms of capital investment and in terms of the additional ongoing maintenance, support and upgrading that is required.
Accordingly, it would be advantageous to provide a method and system that overcomes at least some of the above-mentioned limitations.